Protein remote homology detection is one of fundamental research tasks for downstream analysis (i.e., protein structure and function prediction). Many advanced methods are proposed from different views with complementary detection ability, such as the classification method, the network method, and the ranking method. A framework integrating these heterogeneous methods is urgently desired to reduce the false positive rate and predictive bias. We propose a novel ranking method called ProtRe-CN by fusing the classification methods and network methods via Learning to Rank. Experimental results on the benchmark dataset and the independent dataset show that ProtRe-CN outperforms other existing state-of-the-art predictors. ProtRe-CN improves the detective performance via correcting the false positives in the ranking list by combining the heterogeneous methods. The web server of ProtRe-CN can be accessed at http://bliulab.net/ProtRe-CN .

中文翻译：

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ProtRe-CN：通过学习排序结合分类方法和网络方法检测蛋白质远程同源性

蛋白质远程同源性检测是下游分析（即蛋白质结构和功能预测）的基础研究任务之一。从不同的角度提出了许多检测能力互补的先进方法，如分类法、网络法、排序法等。迫切需要一个集成这些异构方法的框架来降低误报率和预测偏差。我们通过 Learning to Rank 融合了分类方法和网络方法，提出了一种称为 ProtRe-CN 的新型排名方法。在基准数据集和独立数据集上的实验结果表明，ProtRe-CN 优于其他现有的最先进的预测器。ProtRe-CN 通过结合异构方法纠正排名列表中的误报来提高检测性能。ProtRe-CN 的网络服务器可以在http://bliulab.net/ProtRe-CN .